What is Data Fragmentation?
Today, data is widely recognized as a valuable asset, yet most organizations still don’t treat it that way.
Instead, companies often view data as representing challenges such as high storage costs, complex management problems, increasing compliance risk, and even putting a damper on IT morale. One of the key reasons for this is data fragmentation.
The definition of data fragmentation hints at the frustration: data is distributed across different systems and locations, preventing organizations from getting full value from their data.
When data is scattered across many different silos—whether in clouds or on-premises—this fragmentation causes computer capacity to be used inefficiently. Visibility of your data, which is critical for environments that must adhere to regulatory compliance, also becomes difficult. That’s what raises cost, performance, risk, and management issues.
Why Is Data Fragmentation Important?
Data is the most important asset for virtually all organizations. Recently, and with the help of new technologies, businesses have been able to make great strides in collecting, analyzing, organizing, and getting value from their data. Leveraging data strategically is one of the critical drivers of successful digital transformation—which in turn improves productivity, insight, and profits.
Yet, as data grows in different application, storage, geographic, and operational silos as well as in various clouds, teams lose the ability to harness its power and derive full value from it in terms of accurate and meaningful business insights.
This puts businesses at risk of losing competitive advantage. Not only do they fail to monetize their data, but not using it effectively eventually leads to poor customer experience, which directly impacts the bottom line. For these reasons, organizations are working to eliminate mass data fragmentation.
What Is the Main Cause of Data Fragmentation?
Mass data fragmentation is the ever-growing proliferation of data—across different locations, silos, clouds, and management systems—that prevents organizations from fully utilizing its value. Data fragmentation is often accidental as organizations store more and more information to benefit the business. However, when teams no longer have complete visibility into their data, fragmentation becomes a considerable challenge. Infrastructure silos can impact system and operational efficiency.
With no sharing of data between functions, storage cannot easily be optimized. This leads to the generation of multiple copies that take up unnecessary storage space.
Operational efficiency is compromised by the need to manage and coordinate multiple proprietary systems and UIs, each requiring specialist understanding.
This rising volume of fragmented data is also dark—making it almost impossible to see what you have and where it’s stored.
That can raise serious compliance or security risks, and limit storage optimization.
If you don’t know what it is, and where it’s located, how can you know what data must be kept and what can safely be deleted?
These problems can be solved by a next-gen data management solution.
What Are Types of Data Fragmentation?
Data can be fragmented by:
- Locations — In on-premises data centers as well as hybrid, private, or public clouds
- Systems — Databases, servers, edge or IoT devices, for example
- Applications — Backup, file sharing/storage, dev/test, and analytics, for example
What Causes Fragmentation?
There are three primary causes of data fragmentation:
- Fragmentation caused by data volume — Research indicates that data growth is accelerating throughout all organizations. This rate of growth, if not managed correctly, will eventually destroy all normal IT processes, and expose a business’s shortcomings and vulnerabilities. This is because once a business moves beyond its peak capacity, all systems fail.
- Data fragmentation caused by proliferation of data copying — Organizations often make copies of data with all the best intentions, including using it across the business. But making copies not only creates massive additional volumes of data, it also distributes that data everywhere. Eventually, inconsistencies between copies creep in.
- Data fragmentation through data operations — Data also fragments and grows because of all the functional things done to it. Teams protect it by backing it up. They develop and test applications with it. They run analytics on it. And to make matters more complex, most organizations use multiple products from multiple vendors to manage these operations.
How Can Data be Fragmented?
There are three primary ways data can be fragmented:
- Fragmentation exists across and within silos due to organizations using different single-purpose data management products for each of the various data islands that exist.
- Data is also fragmented by having copies of data spread throughout the organization because single-purpose products don’t allow for sharing or reuse.
- Data fragmentation occurs when data distributed across different systems and locations—clouds and/or on-premises—stops organizations from achieving full visibility and control of the data, and from maximizing the potential value of the data. Finally, data fragmentation can occur when data is spread across both on-prem and public and private cloud locations, which causes the creation of even more copies of the same data.
What Is a Fragmentation Example?
An example of data fragmentation would be if ABC Corp. uses multiple vendors to manage data—one for each operational function: data protection (i.e., backup and recovery), development/testing, disaster recovery, etc., and stores the data in a different system, application, or cloud. Each tool makes copies of data for valid operational reasons. But eventually the sheer volume of data through this copying becomes unmanageable. Added to this, one copy of the data may change slightly, then another might change. Soon inconsistencies abound, and there is no single source of truth for ABC Corp.
Cohesity Solves Data Fragmentation
What Cohesity refers to as mass data fragmentation is the huge and growing proliferation of data across a myriad of different locations, infrastructure silos, and management systems.
Exploding data volumes and siloed point products have made it nearly impossible for organizations to protect or locate—let alone manage and exploit—their most important digital asset.
Mass data fragmentation has also become a headache for IT, largely due to lack of innovation by vendors that perpetuate an outdated, and ultimately unsustainable, approach to data storage and management.
Cohesity is unique in the industry when it comes to offering a comprehensive portfolio of data management solutions on-prem and as a service that eliminates mass data fragmentation—something that point or legacy systems are incapable of doing.
Cohesity next-gen data management solutions (available on-prem and as a service) empower organizations to solve mass data fragmentation. Cohesity is simplicity at scale with turnkey data management that eliminates overprovisioning and allows teams to save time and money by redeploying IT staff to more strategic projects.
Often starting with data protection, organizations choose Cohesity to remove data fragmentation across operations from backup and recovery and disaster recovery to archiving, file and object services, dev/test provisioning, data governance, security, and more.